Journal of Korea Society of Industrial Information Systems (한국산업정보학회논문지)

Korea Society of Industrial Information Systems (한국산업정보학회)
권호 표지
DOI QR코드

DOI QR Code

Mobile App for Detecting Canine Skin Diseases Using U-Net Image Segmentation

U-Net 기반 이미지 분할 및 병변 영역 식별을 활용한 반려견 피부질환 검출 모바일 앱

  • Received : 2024.06.29
  • Accepted : 2024.08.15
  • Published : 2024.08.30
Download PDF
⟨ Previous Next ⟩

Abstract

This paper presents the development of a mobile application that detects and identifies canine skin diseases by training a deep learning-based U-Net model to infer the presence and location of skin lesions from images. U-Net, primarily used in medical imaging for image segmentation, is effective in distinguishing specific regions of an image in a polygonal form, making it suitable for identifying lesion areas in dogs. In this study, six major canine skin diseases were defined as classes, and the U-Net model was trained to differentiate among them. The model was then implemented in a mobile app, allowing users to perform lesion analysis and prediction through simple camera shots, with the results provided directly to the user. This enables pet owners to monitor the health of their pets and obtain information that aids in early diagnosis. By providing a quick and accurate diagnostic tool for pet health management through deep learning, this study emphasizes the significance of developing an easily accessible service for home use.

본 논문은 반려견의 피부질환 발병 여부와 부위를 추론하기 위해서 딥러닝 기반 U-Net 모델을 학습하여 이미지 촬영을 통한 반려견의 피부병 발병 여부와 추론되는 병명을 제공하는 애플리케이션을 개발하였다. U-Net은 의료영상 분야에서 주로 사용되는 영역 분할(Image Segmentation) 기반 학습 모델로써 폴리곤 형태의 특정 이미지 영역을 구분하는 데 효과적이다. 따라서 반려견의 피부 이미지에서 병변 영역 식별에 활용할 수 있다. 본 논문에서는 반려견의 6가지 주요 피부질환을 클래스로 정의하고 이를 분별하는 U-Net 모델을 학습시켰다. 이를 모바일 앱으로 구현하여 간단한 카메라 촬영으로 병변 분석과 예측 작업을 수행하여 결과를 제공한다. 이를 통해서 반려인들은 반려동물의 건강 상태를 관찰하고 조기 진단에 도움이 되는 정보를 얻을 수 있다. 이와 같이 딥러닝을 통해서 반려동물 건강관리에 신속하고 정확한 진단 도구를 제공함으로써 가정에서도 손쉽게 이용할 수 있는 서비스 개발에 중요한 의미를 두고 있다.

Keywords

References

  1. Badrinarayanan, V., Kendall, A. and Cipolla, R. (2017). SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation, IEEE Transactions on Pattern Analysis and Machine Intelligence, 39(12), 2481-2495.
  2. Choi, D. and Jang, J. (2021). Design and Performance Evaluation of Deep Learning-based Unmanned Medical Systems for Rehabilitation Medical Assistance, Journal of the Korea Institute of Information and Communication Engineering, 25(12), 1949-1955.
  3. Glanville, C., Hemsworth, P. and Coleman, G. (2020). Conceptualising Dog Owner Motivations: The Pet Care Competency Model and Role of 'Duty of Care', Animal Welfare, 29(3), 271-284.
  4. Hale, H., Blackwell, E., Roberts, C., Roe, E. and Mullan, S. (2023). Broadening the Veterinary Consultation: Dog Owners Want to Talk about More than Physical Health, Animals, 13(3):392, 1-19.
  5. Hwang, J., Choi, C., Nam, K. and Lee, C. (2023). Analysis of Pedestrian Environment Evaluation Factors Using Urban Street View Images Based on Deep Learning, Journal of Korea Society of Industrial Information Systems, 28(6), 45-52.
  6. Hwang, W. K. and Lee, S. A. (2023). Korea Pet Report - Preparation and Health Care for Pets, KB Financial Group Management Research Institute, Customer and Digital.
  7. Jang, Y., Son, J, Jeong, Y. and Ahn, H. (2023). Pet Health and Vaccination Management Community System, Proceedings of KI IT Conference, Nov. 23, Jeju.
  8. Ju, H. I., Kim, Y. M. and Tae, K. S. (2018). Evaluation on the Usefulness of the U-Net Segmentation Model for Auto-Diagnosis of Lumbar Disc Herniation Using Digital Infrared Thermal Imaging, Journal of Rehabilitation Welfare Engineering & Assistive Technology, 12(4), 249-255.
  9. Kang, D., Park, S. and Paik, J. SdBAN (2020). Salient Object Detection Using Bilateral Attention Network With Dice Coefficient Loss, IEEE Access, 8, 104357-104370. https://doi.org/10.1109/ACCESS.2020.2999627.
  10. Kim, B. R., Kim, H. W., Kim, J. H., Zhang, X. F. and Kim, Y. S. (2021). Segmentation of Mass in Mammography using Deep Learning, Journal of Korean Institute of Intelligent Systems, 31(1), 75-81.
  11. Kwon, G., Kim, J. H., Kim, Y. J., Lee, S. M. and Kim, K. G. (2021). Detecting Boundary of Erythema Using Deep Learning, Journal of Korea Multimedia Society, 24(11), 1492-1499.
  12. Lee, H., Lee, W. and Jeong, T. (2020). Deep Learning Algorithm and Prediction Model Associated with Data Transmission of User-Participating Wearable Devices, Journal of Korea Society of Industrial Information Systems, 25(6), 33-45.
  13. Lee, S. and Seo, Y. W. (2022). Exploratory Research on Companion Animal Products and Services - Focusing on Companion Dogs, Journal of the Korea Academia-Industrial cooperation Society, 23(9), 260-267.
  14. Lee, S. M., Lee, S. Y. and Kim, N. G. (2022). Improved Pancreas Segmentation using Multiple Concatenated U-Net Model for Medical Image Systems, Journal of Korean Institute of Information Technology, 20(5), 81-87.
  15. Lee, Y. H., Kim, H. J., Kim, G. B. and Kim, N. K. (2014). Deep Learning-based Feature Extraction for Medical Image Analysis, Journal of the Korean Society of Imaging Informatics in Medicine, 20, 1-12.
  16. Lim, S., Kim, Y. and Kim, K. G. (2020). Three-Dimensional Visualization of Medical Image using Image Segmentation Algorithm based on Deep Learning, Journal of Korea Multimedia Society, 23(3), 468-475.
  17. Lim, T., Lim, K., Chung, S. and Han, S. (2021). Disease Diagnosis Research using Deep Learning based on Military Medical Data, Journal of Digital Contents Society, 22(9), 1359-1367.
  18. Long, J., Shelhamer, E. and Darrell, T. (2015). Fully Convolutional Networks for Semantic Segmentation, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 3431-3440.
  19. Malhotra, P., Gupta, S., Koundal, D., Zaguia, A. and Enbeyle, W. (2022). Deep Neural Networks for Medical Image Segmentation, Journal of Healthcare Engineering, Article ID 9580991, 1-15.
  20. Park, J., Cha, G. and Choi, A. (2023). A 3D ResNet-based Infant Behavior Recognition Technique Using Image Signals, Journal of Korea Society of Industrial Information Systems, 28(3), 1-10.
  21. Pet Skin Disease Data. Available at: https://aihub.or.kr/aihubdata/data/view.do?currMenu=115&topMenu=100&aihubDataSe=realm&dataSetSn=561 (Accessed on Jun 2, 2024).
  22. Ronneberger, O., Fischer, P. and Brox, T. (2015). U-Net: Convolutional Networks for Biomedical Image Segmentation, Medical Image Computing and Computer-Assisted Intervention-M ICCAI 18th International Conference, 9351, 234-241. Oct. 5-9, Munich, Germany.